CUDA Memory Leak
CUDA memory leaks accumulate over training, eventually causing OOM. Denpex tracks allocation patterns to identify leaks.
CUDA memory leaks accumulate over training, eventually causing OOM.
What this failure is
CUDA Memory Leak is a Memory failure seen during ML training runs. CUDA memory leaks accumulate over training, eventually causing OOM. Denpex tracks allocation patterns to identify leaks. Common tags: Cuda, Memory, Leak, Allocation.
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Why it happens (the mechanism)
Python references holding GPU tensors past their useful life. PyTorch caching allocator fragmentation. Custom CUDA operations with manual memory. Hidden allocations in third-party libraries. Taken together, these mechanisms explain why the failure is reproducible, why it tends to surface on specific workloads or scales, and why generic mitigation attempts often fall short without addressing the underlying cause.
What you'll observe
- GPU memory usage increases linearly across training steps
- Memory not released after model deletion
- OOM errors after hours of training that didn't occur at start
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| nvidia-smi shows reserved memory growing across iterations | Python references holding GPU tensors past their useful life |
| torch.cuda.memory_allocated() monotonically increases | PyTorch caching allocator fragmentation |
| No way to free memory without restart | Custom CUDA operations with manual memory |
Which systems are affected
- Long-running training jobs
- Models with complex forward passes
- Custom training loops with manual tensor management
How to confirm this is the problem
Use this checklist to test the hypothesis against a small reproduction. No single line proves the root cause, so preserve the preceding events and compare one variable at a time.
- ✓Reproduce the failure from a clean checkpoint/seed: the symptom must appear without warm-up state from a previous run.
- ✓Verified signal present: nvidia-smi shows reserved memory growing across iterations
- ✓Verified signal present: torch.cuda.memory_allocated() monotonically increases
- ✓Verified signal present: No way to free memory without restart
- ✓A targeted fix from the "How to fix it" section eliminates or substantially reduces the symptom within one validation pass.
The fix and the prevention pattern
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Diagnose this failure in VS Code
Select the traceback or open the failed terminal, then run Denpex locally to see the initiating rank, collateral failures, exact fix, and verification command without uploading the log.
Install the free VS Code extensionCUDA errors in context
CUDA reports errors asynchronously, so the traceback usually points at whatever line synchronised next rather than the one at fault. The hub covers every common CUDA error and how to make it report honestly.
Compare every cuda error side by sideRelated failures to investigate next
Root cause
- Python references holding GPU tensors past their useful life
- PyTorch caching allocator fragmentation
- Custom CUDA operations with manual memory
- Hidden allocations in third-party libraries
The fix and how to prevent it
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Don't just read the fix, diagnose your run
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